Neural network realizations of Bayes decision rules for exponentially distributed data

Igor Vajda; Belomír Lonek; Viktor Nikolov; Arnošt Veselý

Kybernetika (1998)

  • Volume: 34, Issue: 5, page [497]-514
  • ISSN: 0023-5954

Abstract

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For general Bayes decision rules there are considered perceptron approximations based on sufficient statistics inputs. A particular attention is paid to Bayes discrimination and classification. In the case of exponentially distributed data with known model it is shown that a perceptron with one hidden layer is sufficient and the learning is restricted to synaptic weights of the output neuron. If only the dimension of the exponential model is known, then the number of hidden layers will increase by one and also the synaptic weights of neurons from both hidden layers have to be learned.

How to cite

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Vajda, Igor, et al. "Neural network realizations of Bayes decision rules for exponentially distributed data." Kybernetika 34.5 (1998): [497]-514. <http://eudml.org/doc/33384>.

@article{Vajda1998,
abstract = {For general Bayes decision rules there are considered perceptron approximations based on sufficient statistics inputs. A particular attention is paid to Bayes discrimination and classification. In the case of exponentially distributed data with known model it is shown that a perceptron with one hidden layer is sufficient and the learning is restricted to synaptic weights of the output neuron. If only the dimension of the exponential model is known, then the number of hidden layers will increase by one and also the synaptic weights of neurons from both hidden layers have to be learned.},
author = {Vajda, Igor, Lonek, Belomír, Nikolov, Viktor, Veselý, Arnošt},
journal = {Kybernetika},
keywords = {exponentially distributed data},
language = {eng},
number = {5},
pages = {[497]-514},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Neural network realizations of Bayes decision rules for exponentially distributed data},
url = {http://eudml.org/doc/33384},
volume = {34},
year = {1998},
}

TY - JOUR
AU - Vajda, Igor
AU - Lonek, Belomír
AU - Nikolov, Viktor
AU - Veselý, Arnošt
TI - Neural network realizations of Bayes decision rules for exponentially distributed data
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 5
SP - [497]
EP - 514
AB - For general Bayes decision rules there are considered perceptron approximations based on sufficient statistics inputs. A particular attention is paid to Bayes discrimination and classification. In the case of exponentially distributed data with known model it is shown that a perceptron with one hidden layer is sufficient and the learning is restricted to synaptic weights of the output neuron. If only the dimension of the exponential model is known, then the number of hidden layers will increase by one and also the synaptic weights of neurons from both hidden layers have to be learned.
LA - eng
KW - exponentially distributed data
UR - http://eudml.org/doc/33384
ER -

References

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  12. Müller B., Reinhard J., Strickland M. T., Neural Networks, Second edition. Springer, Berlin 1995 
  13. Ripley B. D., Statistical aspects of neural networks, In: Networks and Chaos (O. E. Barndorff–Nielsen, J. L. Jensen and W. S. Kendall, eds.), Chapman and Hall, London 1993. pp. 40–123 (1993) Zbl0825.68531MR1314652
  14. Vajda I., About perceptron realizations of Bayesian decisions about random processes, In: IEEE International Conference on Neural Networks, vol. 1, IEEE, 1996, pp. 253–257 (1996) 

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